Some digital image processing applications designed to enhance the appearance of digital images take explicit advantage of the noise characteristics associated with the source digital images. For example, Keyes et al. in commonly-assigned U.S. Pat. No. 6,118,906 describe a method of sharpening digital images which includes the steps of measuring the noise components in the digital image with a noise estimation system to generate noise estimates and sharpening the digital image with an image sharpening system which uses the noise estimates. Similarly, digital imaging applications have incorporated automatic noise estimation methods for the purpose of reducing the noise in the processed digital images as in the method described by Anderson et al. in U.S. Pat. No. 5,809,178.
In commonly-assigned U.S. Pat. No. 5,923,775, Snyder et al. disclose a method of image processing which includes a step of estimating the noise characteristics of a digital image and using the estimates of the noise characteristics in conjunction with a noise removal system to reduce the amount of noise in the digital image. The method described by Snyder et al. is designed to work for individual digital images and includes a multiple step process for the noise characteristics estimation procedure. A first residual signal is formed from the digital image obtained by applying a spatial filter. This first residual is analyzed to form a mask signal which determines what regions of the digital image are more and less likely to contain image structure content. The last step includes forming a second residual signal and sampling the second residual signal in the image regions unlikely to contain image structure as indicated by the first residual signal. The method taught by Snyder et al. requires the use of the mask signal to produce accurate noise estimates due to the fact that the spatial filter used to calculate the second residual image does not fully filter the image structure content.
It is desirable in any noise estimation method to obtain a residual signal that is pure noise, with no image structure content. This will lead to more accurate estimation of the noise characteristics in the image. Existing techniques suffer from the problem of image structure contamination in the residual signal used to estimate the noise. In other words, the spatial filter that produces the residual signal does not fully filter out image structure. The masking technique can not fully exclude image structure pixels from the residual signal.